The clinical applications of nuclear magnetic resonance imaging (MRI) are progressively expanding into the quantitative measurement of the physical and chemical properties and processes of the human body. Where quantitative information is extracted from the image data, it is important to account for the variety of instrumental effects that may perturb the parameter being measured, as well as any erroneous effects presented by the object being imaged. Of particular importance are the multiplicative inhomogeneities contributing to the spatial variation in radio frequency (RF) signal intensity within the image. Non-uniformities in magnetic resonance signal intensity are caused by both instrumental and object effects, as perceived by J. G. Sled and G. B. Pike, in “Standing-Wave and RF Penetration Artifacts Caused by Elliptic Geometry: An Electrodynamic Analysis of MRI”, IEEE Transactions on Medical Imaging, Vol. 17, No. 4, pp. 653–662, 1998 (ref). Instrumental effects include inhomogeneous RF excitation, non-uniformities in receiving coil sensitivity, and gradient field eddy currents, whilst measurement object effects include variable RF penetration and standing wave effects.
These intensity variations are detrimental to the meaningful comparison of image intensities in separate parts of the image, and must be corrected for if accurate quantitative information is to be obtained. Methods that can correct for the effects of the RF signal intensity gradient are thus an important area of development in quantitative MRI.
A variety of pre- and post-processing techniques have been developed with the aim to correct for the spatial variation in RF signal intensity within the object being measured, as discussed by L. Q. Zhou, Y. M. Zhu, C. Bergot, A.-M. Laval-Jeantet, V. Bousson, J.-D. Laredo, and M. Laval-Jeantet, in “A method of radio-frequency inhomogeneity correction for brain tissue segmentation in MRI”, Computerized Medical Imaging and Graphics, Vol. 25, pp. 379–389, 2001. Pre-processing techniques have focussed on the measurement of homogeneous phantoms prior to measurement of the object under study to estimate the inhomogeneities in the RF field. However, these techniques do not typically account for the inhomogeneities in RF penetration presented by the object being imaged. Post-processing techniques attempt to rectify this situation by estimating the decay profile of the RF signal intensity within the object from analysis of the object data set itself. This enables both correction of object independent effects, such as bias in the RF field, as well as object dependent effects, such as signal intensity attenuation within the object. The assumption behind most post-processing techniques is that the non-uniformity in RF signal intensity may be attributed to a low spatial frequency component throughout the image. A number of approaches to estimating RF inhomogeneities are thus based on intensity correction schemes that employ image smoothing. Homomorphic filters are predominantly used in this situation as they account for the low frequency signal intensity components without altering structural boundaries. Other post-processing methods are based on structural classification techniques and intensity surface interpolation.
One post-processing technique for the estimation of RF signal intensity variations within the human head is worth particular reference. The method by B. M. Dawant, A. P. Zijdenbos, and R. A. Margolin, in “Correction of Intensity Variations in MR Images for Computer-Aided Tissue Classification”, IEEE Transactions on Medical Imaging, Vol. 12, No. 4, pp. 770–781, 1993, consists of interpolating a thin-plate spline surface to reference points within the white matter of the brain and to use the interpolated surface as an estimator of the RF coil profile. The availability of reference points on the periphery of the images is crucial for obtaining good correction surfaces, so intensity values of edge points are extrapolated from the interior reference points. To reduce the sensitivity of the technique to the mislabelling of reference points selected by the user, the number of reference points may be increased by a tissue classification technique. The thin-plate spline surface is then fitted to the reference points by the method of least-squares rather than by interpolation.
The main limitation of the intensity surface correction technique described above is that there is no measured data with which to justify the extrapolated intensity reference values at the periphery of the head, given that the signal intensity gradient is not known at these peripheral points. Further, there is no physical basis to the selection of the thin plate spline surface to model the RF signal intensity gradient. As such, the thin-plate spline surface may not adequately account for local variations in RF attenuation throughout the object being imaged.
It is to be understood that, if any prior art publication is referred to herein, such reference does not constitute an admission that the publication forms a part of the common general knowledge in the art, in Australia or any other country.
In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” is used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.